%J Proceedings 2010 International Symposium on Information Technology - Engineering Technology, ITSim'10
%T A comparison of feed-forward back-propagation and radial basis artificial neural networks: A Monte Carlo study
%V 2
%R 10.1109/ITSIM.2010.5561599
%P 994-998
%A O.A. Abdalla
%A M.N. Zakaria
%A S. Sulaiman
%A W.F.W. Ahmad
%D 2010
%K Artificial Neural Network; Feed-Forward; Feedforward backpropagation; Following problem; Monte Carlo study; Production function; Radial basis; Real problems; Softcomputing techniques; Training algorithms, Information technology; Monte Carlo methods; Neural networks; Soft computing, Backpropagation algorithms
%L scholars1108
%O cited By 13; Conference of 2010 International Symposium on Information Technology, ITSim'10 ; Conference Date: 15 June 2010 Through 17 June 2010; Conference Code:81915
%C Kuala Lumpur
%X Interest in soft computing techniques, such as artificial neural networks (ANN) is growing rapidly. Feed-forward back-propagation and radial basis ANN are the most often used applications in this regard. They have been utilized to solve a number of real problems, although they gained a wide use, however the challenge remains to select the best of them in term of accuracy and efficiency performance. This paper presents a comparison between feed-forward back-propagation and radial basis ANN base on their performance. The comparison is performed using a Monte Carlo study that involves the following problems: addition, multiplication, division, powers and a production function. The result indicates that the proposed radial basis ANN results are significantly better than proposed feed-forward back-propagation ANN results for all five problems. © 2010 IEEE.